Mining association rules for classification using frequent generator itemsets in arules package

被引：2

作者：

Ledmi, Makhlouf ^{[1
]}

Souidi, Mohammed El Habib ^{[1
]}

Hahsler, Michael ^{[2
]}

Ledmi, Abdeldjalil ^{[1
]}

Kara-Mohamed, Chafia ^{[3
]}

机构：

[1] Abbas Laghrour Univ Khenchela, Dept Comp Sci, ICOSI Lab, Khenchela 40000, Algeria

[2] SMU, Bobby B Lyle Sch Engn, Dept Comp Sci, POB 750122, Dallas, TX 75275 USA

[3] Ferhat Abbas Univ Set 1, Dept Comp Sci, LRSD Lab, Setif 19000, Algeria

来源：

INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT | 2023年 / 15卷 / 02期

关键词：

frequent generator itemsets; FGIs; classification; association rules; data mining; R language; EFFICIENT; PATTERNS; REPRESENTATION; TREE;

D O I：

10.1504/IJDMMM.2023.131399

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.

引用

页码：203 / 221

页数：20

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